Time-sensitive multi-task edge calculation and cache cooperative unloading strategy method

Abstract

The invention relates to a time-sensitive multi-task edge calculation and cache cooperation unloading strategy method. The method comprises an unmanned aerial vehicle trajectory optimization sub-strategy, an unmanned aerial vehicle resource allocation optimization sub-strategy and an Internet of Things equipment unloading decision sub-strategy. And the three sub-strategies are iteratively operatedby using a block coordinate descent method, and a final value is obtained after the strategies are finally converged. According to the time-sensitive multi-task mobile edge computing network computing and cache unloading strategy based on the unmanned aerial vehicle, the flight path of the unmanned aerial vehicle, the transmission bandwidth between the Internet of Things equipment and the unmanned aerial vehicle, the computing resource allocation of the unmanned aerial vehicle, the unloading rate of the Internet of Things equipment and the task type of the Internet of Things equipment are comprehensively optimized; the total energy consumption of the IoT device is reduced to the maximum extent, and the experience quality requirement of the IoT device for the time-sensitive task is met.

Description

.Technical field [0001] The invention belongs to the field of unmanned aerial vehicles, edge computing combined with the Internet of Things, under the background of the 5G Internet of Things, a time-sensitive multi-task edge computing and cache cooperative unloading strategy based on the unmanned aerial vehicle. Background technique [0002] Usually the deployment of mobile edge computing servers is fixed, which limits its development closer to IoT devices, thereby further reducing the latency or energy consumption of devices. As an aerial mobile edge computing node, drones are widely used in surveillance, data collection, disaster relief, and public safety service coverage due to their excellent flexibility and mobility. Although drone-enabled mobile edge computing provides IoT devices with remote resources, it still faces communication and computing design challenges due to the limited number of embedded batteries in drones and IoT devices. At present, a variety of energy...

AI Technical Summary

Problems solved by technology

However, offloading energy-consuming workloads to drones also induces additional latency, which can seriously affect the quality of experience of IoT devices, and thus cannot be ignored in policy design

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